Custom AI development for businesses that already have their tools

What does custom AI development mean for a small or mid-sized business?

When a business owner searches for custom AI development, they usually picture something closer to research than to what they actually need. Training or fine-tuning a model is expensive, slow and almost never the bottleneck. The models from Anthropic, OpenAI and Google are already good enough for the large majority of business tasks. What is missing is the plumbing: the model does not know your customers, cannot see your inbox, and has no way to write to your CRM. Custom AI development, in practice, is building that plumbing well.

Three layers show up in almost every build. Integration: the code or workflows that move data between the model and your systems, with authentication, rate limits and retries handled. Agents: a model given a defined job, a set of tools it may call, and rules about what it may and may not do on its own. Retrieval: your documents, past conversations and records indexed so the model answers from your facts instead of from general knowledge. A working system is those three layers plus the operational discipline that keeps it running after the engineer leaves.

What kinds of custom AI builds do I do?

Almost every request I get fits one of five patterns. Each has its own page on this site with more depth, so the descriptions here are one line each. The point of listing them is that most owners do not know which one they need when they start searching, and the discovery call is where we map your process to the right pattern.

The patterns overlap on purpose. A WhatsApp agent usually needs a knowledge base behind it. An n8n integration often includes one ChatGPT or Claude step. The AI agent page covers the general case; the others are the specific entry points people search for.

How do I choose between n8n, a Python service and an agent framework?

The tool is chosen after the process is understood, not before. n8n is the default when the work is mostly moving data between SaaS tools with a few decision points and an occasional model call. It is visual, your team can read it, and it comes with hundreds of integrations already built. I run it self-hosted in my own operation and deploy it for clients on their infrastructure.

A Python service with FastAPI wins when the logic is dense, the volume is high, or the workflow would become a tangle of nodes. A scheduled script that reads a queue, calls the model, validates the output against a schema and writes to Postgres is often forty lines and easier to test than any visual flow. Agent frameworks come in when the model needs to plan across several steps and tools, keep memory, and be observed closely. Most builds mix the three: n8n for the triggers and the SaaS edges, Python for the parts that need real code, an agent loop only where autonomy is actually required.

What I do not do is pick the tool I like and force your problem into it. I am not an n8n certified partner and I do not resell any of this. If your CRM already has the feature, you will hear that on the first call.

What does a custom AI build look like end to end?

Take a common one: inbound requests arriving by email and a web form, each needing to be classified, enriched with what you know about the sender, and answered or routed. The trigger is a webhook from the form and a watch on the shared inbox. The data step looks the sender up in your CRM and pulls the relevant records. The model step, Claude or OpenAI via API, receives a versioned prompt with the request, the CRM context and retrieved passages from your knowledge base, and returns a structured result: category, urgency, a draft reply and a confidence score.

The guardrails sit around that call. Every output is validated against a schema before anything uses it. Low confidence goes to a person. Any action that is irreversible, sending the reply, creating an invoice, changing a deal stage, waits in an approval queue in Slack or in your CRM until someone clicks. Failures are retried with backoff, then logged and alerted. Prompts and workflows are versioned in a repository so a change can be reviewed and rolled back.

It runs in your accounts: your n8n instance or your Railway, GCP or AWS project, your API keys, your Supabase or Postgres. Handoff includes the exported code and workflows in your repository, written documentation and Loom walkthroughs so a new hire can operate it. You own 100% of the code, prompts, workflows and data. Nothing depends on my accounts after the last day.

How much does custom AI development cost?

I do not bill hourly for builds. Every engagement is a single fixed price agreed up front after a discovery call, delivered as a three-week sprint for a scoped build, or three to five weeks for larger ones. The reference points are the three productized systems published on this site: the Lead Acquisition Engine at USD 4,000 setup with an optional USD 490 per month, the AI Intake Agent with knowledge base at USD 4,900 with an optional USD 1,200 per month, and the AI Operating System at USD 6,800 with an optional USD 1,800 per month.

Custom scopes that do not fit one of those are quoted the same way: fixed, in writing, before work starts. The optional monthly covers operations and iteration after the 60-day guarantee ends; it is not required to keep the system running. Infrastructure and API subscriptions (hosting, model usage, n8n Cloud if you choose it) are billed to you directly in your name, so every account is yours from day one. If the honest answer after discovery is that you need a short script rather than a sprint, you will hear that instead of a proposal.

Why does a senior DevOps background matter for AI in production?

The demo is the easy part. Anyone can wire a model to a webhook and show it answering a question. The hard part starts after handoff: the API returns a rate limit at 2 am, a credential expires, a prompt change breaks the output format, the database fills up, a provider deprecates a model version. AI systems fail in the same ways every other production system fails, plus a few new ones, and they need the same discipline.

Ten years of running production infrastructure, Kubernetes certifications and a day job as a Sr DevOps Engineer mean the guardrails are not an afterthought. Every build ships with error handling and retries, structured logs and alerts, secrets management, backups of the database that holds your workflows and data, human approval before anything irreversible, and versioned prompts and workflows. I also run the same stack in my own business every day, an outbound lead engine, a call-to-proposal pipeline and Claude-based agents on n8n, so the defaults come from things that are live, not from a tutorial.

When should you not hire me for custom AI development?

Some searches for custom AI development should end somewhere else, and it is cheaper for both of us to say so early. If you want a model trained on proprietary data from scratch, or a research project with an open-ended outcome, you need a machine learning team, not an integration engineer. If your process is a single form feeding a single spreadsheet, Zapier or a native integration will do and a consultant would be overhead.

I am also not the right fit if you need a large team on site, an agency with account managers and a bench of twenty developers, or a mobile app or public-facing web product where the AI is a small part. I am a solo practitioner who brings in a second engineer for large scopes; you work directly with the person who builds. That is an advantage for scoped systems on top of your stack and a limitation for everything else. If your process has volume, edge cases and real consequences when it fails, that is where I am useful.

How does the engagement work?

It starts with a free 15-minute call to hear the process and decide whether AI belongs in it and whether I am the right person. If there is a fit, you get a written proposal with a closed scope, a timeline and a single fixed price. During the sprint you get weekly updates and approve before anything touches production. Handoff includes documentation, the exported code and workflows in your repository, Loom walkthroughs and a 60-day operational guarantee: if something I built breaks in that window, I fix it at no cost. Optional monthly support after that. I work remotely from Chile with companies in the US, Canada and Europe, in English or Spanish, with full overlap with US business hours.